Research on Appearance Detection, Sorting, and Regrouping Technology of Retired Batteries for Electric Vehicles

Fengdan Liu, Jiangyi Chen (), Dongchen Qin and Tingting Wang
Additional contact information
Fengdan Liu: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
Jiangyi Chen: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
Dongchen Qin: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
Tingting Wang: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China

Sustainability, 2023, vol. 15, issue 21, 1-18

Abstract: The rapid proliferation of electric vehicle adoption has brought about significant changes in energy consumption patterns, but improper disposal of retired batteries poses new challenges to the environment. In order to promote the sustainable development of the industry using retired batteries, this paper focuses on the research on retired battery appearance detection, sorting, and regrouping technologies. Firstly, the standards for retired battery appearance detection are analyzed, and a method of acquiring battery appearance features through two-stage image acquisition is proposed. Machine vision is employed to achieve the appearance filtering of retired batteries, overcoming the shortcomings of manual screening. For the filtered batteries, capacity, internal resistance, and open-circuit voltage are determined as indicators. Analytic hierarchy process and Gray relation analysis are employed for classification based on four application scenarios. Additionally, an improved Gaussian mixture model clustering algorithm is proposed. In the recombination process, the algorithm parameters are adaptively adjusted for each battery category. Experimental results demonstrate that the accuracy of battery appearance filtering exceeds 97%, and the improved algorithm effectively enhances the consistency among batteries. Compared to the baseline algorithm, the performance consistency of regrouping batteries is increased by more than 5%.

Keywords: electric vehicle; retired battery; echelon utilization; appearance detection; sorting and regrouping (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/21/15523/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/21/15523/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:21:p:15523-:d:1272409

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().